{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import xgboost as xgb\n",
    "from pathlib import Path\n",
    "import os, re\n",
    "from datetime import date, datetime\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"C:\\\\Users\\\\raja ajay\\\\Documents\\\\ML competitions\\\\Football Prediction\\\\train.csv\")\n",
    "test_data=pd.read_csv(\"C:\\\\Users\\\\raja ajay\\\\Documents\\\\ML competitions\\\\Football Prediction\\\\test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keeping only relevant columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=train_data[['league','Team 1','Team2','SPI1','SPI2','proj_score1','proj_score2','Outcome']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding categorical data in train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "train_data[['league_en']]=le.fit_transform(train_data['league'])\n",
    "train_data[['Team1_en']]=le.fit_transform(train_data['Team 1'])\n",
    "train_data[['Team2_en']]=le.fit_transform(train_data['Team2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using xGboost to do feature importance & get baseline performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_in_use=['SPI1','SPI2','proj_score1','proj_score2','league_en','Team1_en','Team2_en']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_var = ['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train_data[features_in_use],train_data[y_var] , \n",
    "                                                    test_size=0.33, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=12, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', use_label_encoder=True,\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBClassifier()\n",
    "eval_set = [(X_train, y_train), (X_test, y_test)]\n",
    "#model_xgb.fit(train_data[features_in_use],train_data[y_var])\n",
    "model_xgb.fit(X_train, y_train, eval_metric=\"logloss\", eval_set=eval_set, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'validation_0': OrderedDict([('logloss', [0.447892, 0.310695, 0.223156, 0.164005, 0.122651, 0.093245, 0.07211, 0.056655, 0.045277, 0.036899, 0.03061, 0.025955, 0.022667, 0.019893, 0.017907, 0.016179, 0.014739, 0.013779, 0.012928, 0.012062, 0.011409, 0.01075, 0.010335, 0.009708, 0.009399, 0.008939, 0.008647, 0.008249, 0.007955, 0.00761, 0.00729, 0.007044, 0.0069, 0.006651, 0.006479, 0.006179, 0.005907, 0.005738, 0.00565, 0.005443, 0.005261, 0.005182, 0.005018, 0.004931, 0.004782, 0.004649, 0.004534, 0.004443, 0.004341, 0.004256, 0.004167, 0.004078, 0.00398, 0.003903, 0.003845, 0.003803, 0.003702, 0.003636, 0.003571, 0.003507, 0.003447, 0.00339, 0.003343, 0.00328, 0.00323, 0.003184, 0.003139, 0.003102, 0.003062, 0.003009, 0.002971, 0.002937, 0.002915, 0.002886, 0.00285, 0.002814, 0.002784, 0.002752, 0.002722, 0.002695, 0.002673, 0.002644, 0.002618, 0.002596, 0.002574, 0.002549, 0.002524, 0.002502, 0.002485, 0.002463, 0.002448, 0.002418, 0.002398, 0.002378, 0.002361, 0.002345, 0.002329, 0.002315, 0.002301, 0.002289])]), 'validation_1': OrderedDict([('logloss', [0.451571, 0.317515, 0.232519, 0.175503, 0.134201, 0.105316, 0.084074, 0.068235, 0.057587, 0.049734, 0.044304, 0.039612, 0.036636, 0.033922, 0.032009, 0.030957, 0.029536, 0.028723, 0.028468, 0.027741, 0.027201, 0.026994, 0.026795, 0.026655, 0.026448, 0.026464, 0.026308, 0.026058, 0.025926, 0.025706, 0.025995, 0.025934, 0.025977, 0.025944, 0.025926, 0.026057, 0.025949, 0.026191, 0.026321, 0.026466, 0.026493, 0.02649, 0.026511, 0.026426, 0.026518, 0.026693, 0.026618, 0.026776, 0.027081, 0.027277, 0.027425, 0.027376, 0.027483, 0.027866, 0.027968, 0.027929, 0.027884, 0.028018, 0.02791, 0.028111, 0.027993, 0.02821, 0.028317, 0.028302, 0.0282, 0.028369, 0.028437, 0.028517, 0.028423, 0.028523, 0.028588, 0.02869, 0.028617, 0.028539, 0.028437, 0.028402, 0.028487, 0.02862, 0.028705, 0.028799, 0.02889, 0.028735, 0.028819, 0.028765, 0.028876, 0.029016, 0.029033, 0.029056, 0.029134, 0.029154, 0.02918, 0.029123, 0.029113, 0.029281, 0.029404, 0.029544, 0.02947, 0.029466, 0.029502, 0.029496])])}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = model_xgb.evals_result()\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>proj_score2</td>\n",
       "      <td>64.311867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>proj_score1</td>\n",
       "      <td>30.681908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>league_en</td>\n",
       "      <td>1.491853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPI2</td>\n",
       "      <td>0.987282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Team1_en</td>\n",
       "      <td>0.974939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Team2_en</td>\n",
       "      <td>0.783090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPI1</td>\n",
       "      <td>0.769064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Features  importance\n",
       "3  proj_score2   64.311867\n",
       "2  proj_score1   30.681908\n",
       "4    league_en    1.491853\n",
       "1         SPI2    0.987282\n",
       "5     Team1_en    0.974939\n",
       "6     Team2_en    0.783090\n",
       "0         SPI1    0.769064"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Vars=train_data[features_in_use]\n",
    "feat_imp=pd.DataFrame()\n",
    "feat_imp['Features']=X_Vars.columns.values\n",
    "feat_imp['importance']=model_xgb.feature_importances_ * 100\n",
    "feat_imp=feat_imp.sort_values(by='importance', ascending=False)\n",
    "feat_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using symbolic regression in AutoML Tool to identify model configuration using only Projected scores"
   ]
  },
  {
   "attachments": {
    "Sym_regression.JPG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Sym_regression.JPG](attachment:Sym_regression.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict using final equation for submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "constant=1048.25882147042\n",
    "test_data['pred_term']=((constant*test_data['proj_score1'])-(constant*test_data['proj_score2']))\n",
    "test_data['Outcome']=1/(1+np.exp(-test_data['pred_term']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>league_id</th>\n",
       "      <th>league</th>\n",
       "      <th>Team 1</th>\n",
       "      <th>Team2</th>\n",
       "      <th>SPI1</th>\n",
       "      <th>SPI2</th>\n",
       "      <th>proj_score1</th>\n",
       "      <th>proj_score2</th>\n",
       "      <th>...</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>xg1</th>\n",
       "      <th>xg2</th>\n",
       "      <th>nsxg1</th>\n",
       "      <th>nsxg2</th>\n",
       "      <th>adj_score1</th>\n",
       "      <th>adj_score2</th>\n",
       "      <th>pred_term</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>14/12/21</td>\n",
       "      <td>2411</td>\n",
       "      <td>BPL</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>79.65</td>\n",
       "      <td>74.06</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.19</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>503.164234</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>14/12/21</td>\n",
       "      <td>2411</td>\n",
       "      <td>BPL</td>\n",
       "      <td>Brighton and Hove Albion</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>74.19</td>\n",
       "      <td>71.14</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>387.855764</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>14/12/21</td>\n",
       "      <td>2411</td>\n",
       "      <td>BPL</td>\n",
       "      <td>Norwich City</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>60.67</td>\n",
       "      <td>71.45</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.49</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-324.960235</td>\n",
       "      <td>7.439834e-142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>14/12/21</td>\n",
       "      <td>2411</td>\n",
       "      <td>BPL</td>\n",
       "      <td>Leicester City</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>76.88</td>\n",
       "      <td>79.06</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.44</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.860706</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>14/12/21</td>\n",
       "      <td>2411</td>\n",
       "      <td>BPL</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>63.53</td>\n",
       "      <td>85.58</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.92</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1016.811057</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season      date  league_id league                    Team 1  \\\n",
       "0    2021  14/12/21       2411    BPL                   Arsenal   \n",
       "1    2021  14/12/21       2411    BPL  Brighton and Hove Albion   \n",
       "2    2021  14/12/21       2411    BPL              Norwich City   \n",
       "3    2021  14/12/21       2411    BPL            Leicester City   \n",
       "4    2021  14/12/21       2411    BPL                 Brentford   \n",
       "\n",
       "               Team2   SPI1   SPI2  proj_score1  proj_score2  ...  score1  \\\n",
       "0    West Ham United  79.65  74.06         1.67         1.19  ...     NaN   \n",
       "1      Wolverhampton  74.19  71.14         1.35         0.98  ...     NaN   \n",
       "2        Aston Villa  60.67  71.45         1.18         1.49  ...     NaN   \n",
       "3  Tottenham Hotspur  76.88  79.06         1.52         1.44  ...     NaN   \n",
       "4  Manchester United  63.53  85.58         0.95         1.92  ...     NaN   \n",
       "\n",
       "   score2  xg1  xg2  nsxg1  nsxg2  adj_score1  adj_score2    pred_term  \\\n",
       "0     NaN  NaN  NaN    NaN    NaN         NaN         NaN   503.164234   \n",
       "1     NaN  NaN  NaN    NaN    NaN         NaN         NaN   387.855764   \n",
       "2     NaN  NaN  NaN    NaN    NaN         NaN         NaN  -324.960235   \n",
       "3     NaN  NaN  NaN    NaN    NaN         NaN         NaN    83.860706   \n",
       "4     NaN  NaN  NaN    NaN    NaN         NaN         NaN -1016.811057   \n",
       "\n",
       "         Outcome  \n",
       "0   1.000000e+00  \n",
       "1   1.000000e+00  \n",
       "2  7.439834e-142  \n",
       "3   1.000000e+00  \n",
       "4   0.000000e+00  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df=test_data[['Outcome']]\n",
    "submission_df.to_csv('my_submission_file.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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